Citations

Background: Immune checkpoint inhibitors (ICIs) are a first-line and perioperative treatment for lung cancer. Pneumonitis is a potentially life-threatening complication of ICI treatment in 2% to 5% of patients; however, risk factors for developing ICI pneumonitis (ICI-p) remain undefined. Methods: We conducted a retrospective cohort study of consecutive patients with lung cancer who received at least one dose of ICI from 2015 through 2020 at The Ohio State University. Pneumonitis cases were documented by the treating oncologist and retrospectively evaluated for agreement between an oncologist and a pulmonologist. Patient demographic and clinical characteristics were recorded and summarized between those with and without pneumonitis for the overall cohort. Univariate and multivariable survival analyses using the Fine-Gray competing risk model were used to examine the associations. Results: A total of 471 patients with lung cancer were included, of which 402 had non–small cell lung cancer and 69 had small cell lung cancer; 39 (8%) patients in the overall cohort developed ICI-p. Preexisting interstitial abnormalities and prior chest radiation were both significantly associated with ICI-p on univariate analysis (hazard ratio [HR], 8.91; 95% CI, 4.69–16.92; P<.001; and HR, 2.81; 95% CI, 1.50–5.28; P=.001). On multivariable analyses, interstitial abnormalities remained a strong independent risk factor for ICI-p when controlling for chest radiation and type of immunotherapy (HR, 9.77; 95% CI, 5.17–18.46; P<.001). Among patients with ICI-p (n=39), those with severe (grade 3–5) pneumonitis had worse overall survival compared with those with mild (grade 1 or 2) pneumonitis (P=.001). Abnormal pulmonary function test results at both 12 and 18 months prior to ICI initiation were not significantly associated with ICI-p. Conclusions: Preexisting interstitial abnormalities on chest CT and prior chest radiation are independent risk factors that are strongly associated with ICI-p in patients with lung cancer. These findings highlight a potential need for closer observation for ICI-p among patients with these risk factors.

Introduction: Development of new intraventricular hemorrhage (IVH) within 24 hours of presentation or expansion of 1mL or more independently predicts poor outcome. Current methods for IVH volume estimation lack sufficient precision to detect this level of change. The best method of quantification with manual/semi-automated segmentation remain clinically impractical. We aimed to train and validate a convolutional neural network (CNN) to autonomously segment and quantify IVH volume in acute intracerebral hemorrhage (ICH) patients. Methods: Imaging data for training and validation was obtained from 3 international multicenter studies. IVH volumes were assessed using CNN, IVH Score (IVHS), Original Graeb Score (oGS), Modified Graeb Score (mGS), and manual segmentation using ITK-SNAP. Agreement between CNN volume and IVHS estimated volume compared to manual segmentation was assessed using intraclass correlation coefficient (ICC) and Bland-Altman charts. Accuracy of CNN segmentation was compared to manual segmentation with Dice similarity coefficient (DSC). Accuracy of hematoma expansion detection was assessed using receiver operating characteristic curves. Results: A total of 172 patients and 311 CT scans were included. Intra-rater reliability was significantly greater for CNN versus IVHS method (ICC 0.99 [95%CI 0.99 - 1.00] vs 0.76 [95%CI 0.66 - 0.83]). Accuracy of CNN segmentation was satisfactory (DSC 0.76 [95%CI 0.75 - 0.78]). Accuracy of hematoma expansion detection was substantially greater in CNN (AUC 0.91 [95%CI 0.85 - 0.97]) versus IVHS, oGS and mGS. Conclusion: Our results demonstrate that a fully automated CNN algorithm is capable of segmenting IVH volumes on multi-center data with higher intra-rater reliability and satisfactory accuracy over the current methods of IVH volume measurement tools. The algorithm has proven to be capable of detecting hematoma expansion with substantially greater accuracy over existing methods of IVH measurement.